This paper presents a noise adaptive variational Bayesian cubature information filter based on Wishart distribution. In the frame of recursive Bayesian estimation, the noise adaptive information filter propagating the information matrix and information state is derived. And the integration of recursive Bayesian estimation is approximated by cubature integration rule. Then, the inverse of measurement noise matrix is modeled as a Wishart distribution, so the joint distribution of posterior state and measurement noise can be approximated by the product of independent Gaussian and Wishart. Furthermore, the corresponding square root version is also derived to improve numerical characteristics. Simulation results with unknown and correlated measurement noise demonstrate the effectiveness of the proposed algorithms.

The traditional consensus-based filters are widely used in distributed sensor networks. However, they suffer from divergence when outliers occur. This paper proposes a robust consensus nonlinear information filter for distributed state estimation with measurement outliers. Unlike the Gaussian assumption in traditional consensus filers, the measurement of each sensor node is modeled here as a multivariate Student-t process with unknown parameters of the sufficient statistic. The variational Bayesian method is employed to jointly estimate the state and the parameters. As the state and parameters are coupled, the updated equation can be solved by fixed point iteration. The centralized outliers robust information filter is first derived for multiple sensors. It is then extended to a distributed version to fuse information from multiple interconnected local estimators. The integral of the consensus-based nonlinear filter is approximated by Gaussian approximation under the framework of the information filter. The consensuses are based on both likelihoods and prior probability distributions. The consensus and convergence of the proposed method are also analyzed. Simulation results show that the proposed approach is effective in dealing with outliers.

Unmanned Aerial Manipulators (UAMs) have much potential for automating the cleaning of the high-rise windows. In this letter, a switchable UAM system is designed to carry out the high-rise cleaning task by installing a cleaning robot onto the window. The UAM system is divided into four phases: free-flight, attaching, delivery and detaching. Control methods are developed for all phases, and switch strategies between different phases are proposed. The results of outdoor window-cleaning robot installation experiments using the proposed UAM system demonstrate the effectiveness of our control methods and switch strategies.

This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the “label space mismatching” phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.

In this paper, we consider the position control problem of nonholonomic mobile robots, i.e., the regulation of mobile robots to a desired position specified by the image position of a feature point onboard the mobile robot, by using visual feedback information from an overhead fixed camera. Many vision-based control approaches have been proposed for motion control of nonholonomic mobile robots, however, they usually assume that exact or approximate knowledge of full/partial intrinsic and/or extrinsic parameters of the camera can be available, and large or even very small errors in these parameters can deteriorate the control performance, reduce the control accuracy, and even affect the system stability. Hence, it is interesting and highly desirable to develop vision-based controllers without depending on the camera parameters, from both control performance and controller implementation perspectives. With this in mind, we propose novel image-based position control schemes for nonholonomic mobile robots completely without knowing the camera parameters. In the proposed schemes, neither accurate nor approximate knowledge about the camera intrinsic and extrinsic paramet